EP4576014A1 - Verfahren und vorrichtung zur betrugsbestimmung in einem biometrischen bilderkennungssystem - Google Patents

Verfahren und vorrichtung zur betrugsbestimmung in einem biometrischen bilderkennungssystem Download PDF

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Publication number
EP4576014A1
EP4576014A1 EP24203591.3A EP24203591A EP4576014A1 EP 4576014 A1 EP4576014 A1 EP 4576014A1 EP 24203591 A EP24203591 A EP 24203591A EP 4576014 A1 EP4576014 A1 EP 4576014A1
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EP
European Patent Office
Prior art keywords
image
images
vector representation
wavelength band
fraud
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP24203591.3A
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English (en)
French (fr)
Inventor
Jean Beaudet
Loïc JEZEQUEL
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IDEMIA Public Security SAS
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IDEMIA Public Security SAS
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Publication of EP4576014A1 publication Critical patent/EP4576014A1/de
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/40—Spoof detection, e.g. liveness detection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/761—Proximity, similarity or dissimilarity measures
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition
    • G06V10/12—Details of acquisition arrangements; Constructional details thereof
    • G06V10/14—Optical characteristics of the device performing the acquisition or on the illumination arrangements
    • G06V10/143—Sensing or illuminating at different wavelengths
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/172—Classification, e.g. identification
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/10—Cameras or camera modules comprising electronic image sensors; Control thereof for generating image signals from different wavelengths
    • H04N23/11—Cameras or camera modules comprising electronic image sensors; Control thereof for generating image signals from different wavelengths for generating image signals from visible and infrared light wavelengths

Definitions

  • the invention relates to a method for determining fraud in a biometric image recognition system based on the analysis of at least two images of the same object, for example a face, obtained in at least two distinct wavelength bands.
  • biometric human recognition systems are based on analyzing images of the person. Typically, a specific part of the person, the area of interest, is used for recognition. This can be the face, but also the person's fingerprint or iris, but this list is not exhaustive.
  • One way to make these systems more robust is to base recognition on a pair of images of the area of interest obtained in different wavelength bands, for example, a first image is obtained in the visible spectrum and a second image is obtained in the infrared spectrum. This is an example; other wavelength bands can be considered.
  • the two images are then submitted to a fraud detector, separately or concatenated.
  • a fraud detector separately or concatenated.
  • a face image presented in place of the person's real face, on a photograph or a screen can produce a realistic image of the face in the visible spectrum but a black image in the infrared spectrum.
  • the fraud detector is typically a classifier using neural network technology trained to recognize fraud attempts.
  • a first classifier may be specialized on the visible spectrum whereas a second classifier can be specialized on the infrared spectrum.
  • the result of the two classifiers is then consolidated to obtain the final result of the fraud detection.
  • the two images are concatenated.
  • the infrared image is added as an additional component to the red, green and blue components of the visible image.
  • the infrared image replaces one of the components to obtain, for example, an RGIR image, that is to say red, green and infrared. It is then this image combining the two different wavelength bands that is provided as input to a single classifier to detect fraud.
  • the invention proposes to subject the input images to encoding.
  • Each image is encoded using an encoder based on a neural network.
  • the result of the encoding is a vector of data representative of the image.
  • Fraud is then detected by calculating the similarity between the encoded representations of the images in each wavelength band.
  • the encoders are trained jointly to maximize the similarity of the encoded representations of the images from the same person and minimize the similarity when this is not the case.
  • This approach allows for better fraud detection results compared to known classifier-based systems.
  • the risk that fraudulent images produce similar representations is low.
  • the invention improves robustness to shooting conditions.
  • the first image and the second image are captured at the same time.
  • the first image and the second image are captured by the same camera on the same sensor.
  • the similarity measure is a normalized dot product.
  • Fraud attempts are primarily aimed at deceiving the recognition system.
  • a person attempts to be identified by the system as someone else. For example, a person presents a photograph of an authorized person to attempt to gain access to a building or room under the control of the recognition system. This photograph may be presented as a "paper" image or displayed on a tablet screen, for example.
  • the input image 101 can then have a fourth component added corresponding to the image in the infrared domain; this is then referred to as a four-component RGBIR image.
  • the infrared image replaces one of the components of the visible image, for example blue; this is then referred to as an RGBIR image.
  • the input image is then a combined image, combining the two wavelength domains.
  • visible and infrared images are not combined.
  • the system illustrated in Figure 1 is then duplicated.
  • One system processes the visible image and the second processes the infrared image, the result of both systems is used to obtain the final result.
  • a single camera is used, allowing both wavelength bands to be obtained on a single sensor.
  • This solution gives the best results because it guarantees synchronization and uniqueness of the subject's pose between the two images.
  • the input image 101 is provided to an encoder 102 to obtain a vector representation of the input image.
  • This encoder is a neural network, for example using the architecture called EfficientNet described in the article: “EfficientNet: Rethinking Model Scaling for Convolutional Neuronal Networks” by Mingxing Tan and Quoc V. Le.
  • the vector representation of the image is then submitted to a classifier 103 to produce a result 104.
  • the classifier is also a neural network.
  • the result is binary and gives the system's determination of the authenticity of the input image 101, namely whether this image is fraudulent or not.
  • the result is a real number, for example between 0 and 1 which gives a probability of fraud on the input image 101.
  • Such a system is trained on a set of images known to be authentic (non-fraudulent) and images known to be fraudulent.
  • the fraudulent images used during training are generated from known fraud techniques. This is one of the weaknesses of these systems. Faced with a new fraud technique not considered during the training of the system, the result is not predictable.
  • the system according to the invention then improves the performance of such systems and makes it less sensitive to changes in pose and facial expression.
  • FIG. 2 illustrates a fraud detection system according to one embodiment of the invention.
  • This system takes as input two images 201 and 211 in two different wavelength bands of the subject and more precisely of the area of interest of the subject.
  • these images are taken at the same time minimizing the difference in viewing angle.
  • the best results are obtained with a single camera allowing simultaneous shooting of both wavelength bands by the same sensor. This guarantees perfect synchronous shots and exactly the same viewing angle.
  • a single camera which produces an RGIR image, i.e. comprising three components, a red component, a green component and an infrared component.
  • Each image is then encoded by an encoder 202, 212, specialized for the wavelength band used.
  • the encoder 202 is specialized for encoding RG images, while the encoder 212 is specialized for infrared images.
  • the specialization of the encoders is obtained by training these neural networks on images in the given wavelength band.
  • the output 203, 213 of each encoder is a vector representation of their respective input image 201, 211.
  • the decision 204 as to the fraudulent aspect of the input images is here deduced from a similarity measure 202 between the two vector representations 203, 213 associated with the two input images in their respective wavelength bands.
  • the similarity measurement between the vector representations 203, 213 of the input images is performed, for example, by a normalized scalar product of the vectors constituting these vector representations.
  • This normalized scalar product corresponds to a cosine between the directions of the vectors.
  • the result of the scalar product is then compared to a predefined threshold above which the similarity between the vectors is considered to determine authentic images. Below the threshold, the input images are considered fraudulent.
  • the threshold value is set to 0.38.
  • any other measure of similarity between vectors can be used as an alternative to the normalized dot product, for example a measure of the Euclidean distance between vectors.
  • the process thus implemented has the advantage of being able to detect fraudulent images that would be generated using new fraud techniques. Indeed, it is unlikely that the vector representations resulting from these new fraud techniques will be similar in the two wavelength bands analyzed.
  • the method thus described can be generalized to more than two wavelength bands by generating a vector representation of each analyzed band.
  • the similarity measurement can then be made two by two between the vector representations obtained.
  • the average of the vector representations can be calculated, then the scalar products between the vectors and this average calculated.
  • the production of the result can then be done by comparing each similarity measurement to the predefined threshold, the images being determined as authentic if none of the similarity measurements exceed the threshold.
  • an average of the similarity measurements can be compared to the predefined threshold to obtain the result.
  • FIGS. 3a and 3b illustrate the learning of encoders 202 and 212 used by the fraud determination system illustrated by the Figure 2 .
  • the system of the Figure 3a takes the input images 301 and 311 corresponding to the input images 201 and 211, the encoders 302 and 312 corresponding to the encoders 302 and 312, to generate the vector representations 303 and 313.
  • the two encoders 302 and 312 are trained jointly so as to minimize or maximize the similarity 304 of the vector representations 303 and 313 as a function of the input images 301 and 311.
  • FIG. 3b illustrates the processing of input images during system training.
  • Image pairs (321, 331), (322, 332) and (323, 333) are input image pairs used during training.
  • Image pairs (321, 331) and (322, 332) are authentic image pairs, while image pair (323, 333) is fraudulent.
  • Training consists of maximizing the similarity of the vector representations of images 321 and 331 on the one hand, as well as the similarity of the vector representations of images 322 and 332 on the other hand. This is illustrated by links 341. Conversely, the similarity of the vector representations of images 321 and 332, 322 and 331, 322 and 333, 323 and 332, 323 and 333 is minimized.
  • the second image is encoded using a second neural encoder to produce a vector representation of the second image.
  • This second neural encoder is trained on images corresponding to the second wavelength band.
  • the vector representations of the two images are compared by a similarity function, for example a normalized scalar product. The result of this similarity function is used to determine whether the input images are authentic or, on the contrary, fraudulent. If the vector representations are sufficiently similar, for example by comparing the result of the similarity function to a threshold, it is determined that the input images are authentic.

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • Databases & Information Systems (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Human Computer Interaction (AREA)
  • Signal Processing (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Collating Specific Patterns (AREA)
EP24203591.3A 2023-12-20 2024-09-30 Verfahren und vorrichtung zur betrugsbestimmung in einem biometrischen bilderkennungssystem Pending EP4576014A1 (de)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
FR2314615A FR3157636B1 (fr) 2023-12-20 2023-12-20 Procédé et dispositif de détermination de fraudes dans un système de reconnaissance d’images biométriques.

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EP4576014A1 true EP4576014A1 (de) 2025-06-25

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US (1) US20250209785A1 (de)
EP (1) EP4576014A1 (de)
FR (1) FR3157636B1 (de)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180039845A1 (en) * 2016-08-08 2018-02-08 International Business Machines Corporation Method and apparatus to identify a live face image using a thermal radiation sensor and a visual radiation sensor
US11842573B1 (en) * 2023-07-21 2023-12-12 Raphael A. Rodriguez Methods and systems for enhancing liveness detection of image data

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180039845A1 (en) * 2016-08-08 2018-02-08 International Business Machines Corporation Method and apparatus to identify a live face image using a thermal radiation sensor and a visual radiation sensor
US11842573B1 (en) * 2023-07-21 2023-12-12 Raphael A. Rodriguez Methods and systems for enhancing liveness detection of image data

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FR3157636B1 (fr) 2026-05-01
FR3157636A1 (fr) 2025-06-27
US20250209785A1 (en) 2025-06-26

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